Field of the Invention
The present invention relates to an image processing apparatus and method suitable for medical images captured by various medical image collection apparatuses (modalities) such as an MRI (Magnetic Resonance Imaging) apparatus, X-ray CT (X-ray Computed Tomography) apparatus, and US (ultrasonic image diagnosis) apparatus.
Description of the Related Art
In a medical field, when an area of interest is found on an image of a certain modality, an area corresponding to that area of interest (corresponding area) is identified on an image of another modality, and diagnosis is often given by comparing the two areas. When these modalities capture images to have the same body position, these areas can be easily identified and compared. However, when these modalities capture images to have different body positions, since shapes of subjects are different at capturing timings, it becomes difficult to identify and compare the areas. Hence, an attempt is made to estimate deformations of both the subject (that is, to do deformable image alignment). Then, it becomes possible to estimate a position of the corresponding area based on position information of the area of interest, and to apply deformation to one image to generate an image having the same shape as that of the other image.
For example, Reference 1 (T. Carter, C. Tanner, N. Beechey-Newman, D. Barratt and D. Hawkes, “MR navigated breast surgery: Method and initial clinical experience,” MICCAI2008) discloses a technique for aligning and displaying an MRI image captured at a prone position and an ultrasonic image captured at a supine position. More specifically, a finite element model (FEM) is generated based on the MRI image captured at the prone position, and a deformation simulation from the prone position to the supine position is executed using this model. Then, based on this simulation result, the MRI image captured at the prone position, a lesion area drawn in the MRI image, and the like are superimposed on the ultrasonic image at the supine position. Using this display, position differences caused by the deformation between the prone position and supine position can be corrected.
Also, Reference 2 (Y. Hu, D. Morgan, H. Ahmed, D. Pendse, M. Sahu, C. Allen, M. Emberton and D. Hawkes, “A Statistical Motion Model Based on Biomechanical Simulations,” MICCAI2008) discloses a technique which can cope with a case in which material characteristics and border conditions of a target are not clear by executing a deformation simulation using a finite element model. More specifically, deformation simulations are executed in advance under the assumption of various material characteristics and border conditions, and a model which expresses a deformation of the target using a relatively small number of coefficients is generated from a group obtained as a result of the simulations. Then, a deformation of the target is estimated using that model.
When the technique described in Reference 1 is used, deformation estimation with high precision between the prone position and supine position can be expected. However, since the deformation simulation is required to be repetitively executed, the deformation estimation processing requires much time. On the other hand, when the technique described in Reference 2 is used, the deformation estimation processing time can be shortened. However, a finite element model has to be generated in association with a target case, and that operation is troublesome.